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CPP 32: Condensed-matter simulations augmented by advanced statistical methodologies (joint session DY/CPP)
Monday, March 16, 2020, 15:30–18:15, HÜL 186
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15:30 |
CPP 32.1 |
Funnel Hopping Monte Carlo: An efficient method to overcome broken ergodicity — •Jonas Alexander Finkler and Stefan Goedecker
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15:45 |
CPP 32.2 |
Second-principles investigation of the electrocaloric properties of PbTiO3 — •Monica Graf and Jorge Iñiguez
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16:00 |
CPP 32.3 |
Exploring Chemical Reaction Space with Machine Learning — •Sina Stocker, Gábor Csányi, Karsten Reuter, and Johannes T. Margraf
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16:15 |
CPP 32.4 |
Kernel-based machine learning for efficient molecular liquid simulations — •Christoph Scherer, René Scheid, Tristan Bereau, and Denis Andrienko
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16:30 |
CPP 32.5 |
Anharmonic phonons sampled from large scale molecular dynamics based on on-the-fly machine- learning force fields — •Jonathan Lahnsteiner and Menno Bokdam
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16:45 |
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15 min. break.
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17:00 |
CPP 32.6 |
Edgy and Parallel – Efficient Equilibration of Anisotropic Hard Particulate Systems — •Marco Klement and Michael Engel
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17:15 |
CPP 32.7 |
Machine-learning force fields trained on-the-fly with bayesian inference — Ryosuke Jinnouchi, Jonathan Lahnsteiner, Ferenc Karsai, Georg Kresse, and •Menno Bokdam
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17:30 |
CPP 32.8 |
Learning effective collective variables for biasing via t-distributed stochastic neighbor embedding — •Omar Valsson and Jakub Rydzewski
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17:45 |
CPP 32.9 |
Variational autoencoders as a tool to learn collective variables from simulation snapshots — •Miriam Klopotek and Martin Oettel
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18:00 |
CPP 32.10 |
Adversarial Reverse Mapping of Equilibrated Condensed-Phase Molecular Structures — •Marc Stieffenhofer, Michael Wand, and Tristan Bereau
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